Applied Computational Psychiatry
Our vision is to develop computational tools with real potential for clinical applications.
When an organ is unable to meet the demands placed on it, illness can arise. As the main functions of the brain are to compute and learn, an understanding of mental illnesses will benefit from an understanding of the computational and learning functions the brain performs, and how these are affected in states of ill-health.
The Applied Computational Psychiatry group focuses on developing computational tools with real potential for clinical applications. While we are fascinated by the brain and by computational methods in general, our research invests most in those aspects which we think are most likely to result in treatments.
- The use of computational modelling, neuroimaging and behaviour – to understand what happens when patients stop taking antidepressant and how this leads to relapse. We then attempt to use this knowledge to predict who will relapse and hence aid clinical decision-making.
- The use of computational modelling, neuroimaging and behaviour to understand how decision-making and learning contribute to the development, maintenance and relapse in alcohol dependence.
- Development of MEG-decoding approaches to understand automatic negative thoughts.
- Development of computational methods to understand affect dynamics.
- Explaining distortions in metacognition with an attractor network model of decision uncertainty PLOS COMPUTATIONAL BIOLOGY, 17 (7) DOI: 10.1371/journal.pcbi.1009201
- How Representative are Neuroimaging Samples? Large-Scale Evidence for Trait Anxiety Differences Between fMRI and Behaviour-Only Research Participants. Social Cognitive and Affective Neuroscience DOI: 10.1093/scan/nsab057
- View all publications by the Applied Computational Psychiatry team